Key Information
Tutors: Dr John Pinney
Duration: 3 x 2 hour sessions
Delivery: Live (Online)
Course Credit (PGR only): 1 credit
Audience: Research Degree Students, Postdocs, Research Fellows
Dates
- 25 Nov, 02 & 09 Dec 2025
14:00-16:00, MS Teams - 04, 11 & 18 February 2026
10:00-12:00, MS Teams - 21, 28 May & 04 June 2026
14:00-16:00, MS Teams
Course Resources
Following on from the Introduction to Machine Learning course, this series of hands-on workshops will get you started with applying supervised and unsupervised machine learning methods in Python, using the popular scikit-learn package.
This course is open to Research Degree Students, Postdocs & Research Fellows. Limited spaces available for wider Imperial community.
Learning Outcomes:
After completing this workshop, you will be better able to:
- Prepare a dataset for machine learning in Python
- Select a scikit-learn method appropriate for a particular learning task
- Construct your own workflows for model training and testing
- Evaluate the performance of a model
Prerequisites:
- Introduction to Python for Researchers (Online Course) (or equivalent prior learning)
- Introduction to Machine Learning (or equivalent prior learning)
How to book
- Early Career Researchers (Research Degree Students, Postdocs, Research Fellows) should book via Inkpath using your Imperial Single-Sign-On.
- All other members of the Imperial community, should book here.
Please ensure you have read and understood ECRI’s cancellation policy before booking.